Individual alcohol consumption by the population of Russia versus regional living conditions

Background:There are substantial regional differences in drinking alcohol in Russia,both at the population and individual levels.However,the causes of these differences have not been studied yet.Objective:The goal of our study was to examine the effect of regional living conditions on individual alcohol consumption by the population of Russia.Methods:For the analysis,we used data from a cross-sectional epidemiological study conducted in 2013-2014.The final analytical sample included 18,130 people aged 25-64 years.We conducted the interviews face to face,based on which any drinking in the last year,as well as alcohol abuse,were considered as a response.Alcohol abuse was recorded when the respondent consumed 5.75 or more grams of pure ethanol per day(75th percentile of average daily alcohol consumption among alcohol drinkers).The assessment of the regional living conditions was accomplished via integral indexing,which was previously performed based on publicly available data for 2010-2014.Associations were assessed using generalized scoring equations with unchanging standard errors.The associations were expressed by odds ratios(OR)and 95%confidence intervals(C).Results:Deterioration of social conditions and increase in demographic depression in the region of residence increased the odds of any drinking(OR 1.51,95%CI:1.33 to 1.72,P<0.001 and OR 1.22,95%CI:1.05 to 1.41,P=0.oo9,respectively).The odds of alcohol abuse increased with the deterioration of social living conditions and the growth of the industrial development in the region:OR 1.35,95%CI:1.14 to 1.59,P<0.001 and OR 1.16,95%CI:1.05 to 1.28,P=0.002,respectively.Conclusion:Our analysis allowed assessing the impact of the regional living conditions on individual drinking alcohol in the population of Russia.

Fine-Grained Features for Image Captioning

Image captioning involves two different major modalities(image and sentence)that convert a given image into a language that adheres to visual semantics.Almost all methods first extract image features to reduce the difficulty of visual semantic embedding and then use the caption model to generate fluent sentences.The Convolutional Neural Network(CNN)is often used to extract image features in image captioning,and the use of object detection networks to extract region features has achieved great success.However,the region features retrieved by this method are object-level and do not pay attention to fine-grained details because of the detection model’s limitation.We offer an approach to address this issue that more properly generates captions by fusing fine-grained features and region features.First,we extract fine-grained features using a panoramic segmentation algorithm.Second,we suggest two fusion methods and contrast their fusion outcomes.An X-linear Attention Network(X-LAN)serves as the foundation for both fusion methods.According to experimental findings on the COCO dataset,the two-branch fusion approach is superior.It is important to note that on the COCO Karpathy test split,CIDEr is increased up to 134.3%in comparison to the baseline,highlighting the potency and viability of our method.

REGIONAL FEATURES OF LONG-TERM SST VARIATION IN THE WESTERN PACIFIC WARM POOL AREA

The main features of SST interannual and long term variations in the Western Pacific Warm Pool area were studied by using regression analysis, running t test and spectral analysis methods based on monthly mean SST data in the Pacific during 1950-1998. The results showed that the SST inter annual and long term variations in the Western Pacific Warm Pool area had evident regional features. There were significant differences in variation range, phase, period, occurrence time of abrupt variation between SST in the eastern area (east of 160°E) and SST in the western area (west of 160°E).

MVCE-Net: Multi-View Region Feature and Caption Enhancement Co-Attention Network for Visual Question Answering

Visual question answering(VQA)requires a deep understanding of images and their corresponding textual questions to answer questions about images more accurately.However,existing models tend to ignore the implicit knowledge in the images and focus only on the visual information in the images,which limits the understanding depth of the image content.The images contain more than just visual objects,some images contain textual information about the scene,and slightly more complex images contain relationships between individual visual objects.Firstly,this paper proposes a model using image description for feature enhancement.This model encodes images and their descriptions separately based on the question-guided coattention mechanism.This mechanism increases the feature representation of the model,enhancing the model’s ability for reasoning.In addition,this paper improves the bottom-up attention model by obtaining two image region features.After obtaining the two visual features and the spatial position information corresponding to each feature,concatenating the two features as the final image feature can better represent an image.Finally,the obtained spatial position information is processed to enable the model to perceive the size and relative position of each object in the image.Our best single model delivers a 74.16%overall accuracy on the VQA 2.0 dataset,our model even outperforms some multi-modal pre-training models with fewer images and a shorter time.

Geochemical Features of a Subduction Initiation Ophiolite Type in Habana-Matanzas Region(Western Cuba)

We present new geochemical data for the upper mantle and crustal sections(whole-rock major and trace element compositions)as well as mineral chemical data,from the Northern Carbibbean ophiolites in the Habana-Matanzas region in Western Cuba.These ophiolites are part of the Northern Cuban Ophiolitic Belt(NCOB),extending for more than 1000 km along the island.The upper mantle peridotites are composed mainly of refractory harzburgite with tectonite textures,and show convex-downward patterns depleted in MREE normalized to chondrite values(Mc Donough and Sun,1995).These geochemical trends are characteristic for depleted mantle wedge peridotites metasomatized by slab-derived,LREE enriched melts.The NCOB also includes abyssal peridotites having lower LREE/HREEratiosanddisplayingrelativelyhomogeneous and flat patterns from MREE to HREE.These peridotites represent fragments accreted into the continental margin from a subducted oceanic lithosphere.Gabbro and dolerite units in the NCOB are systematically depleted in High Field Strength Elements(HFSE:Nb,Ta,Hf,Ti)and REE with respect to N-MORB(<1 X N-MORB).Their melt evolution was affected by subduction input.Spatially associated granitic rocks have a volcanic arc geochemical affinity.Some mafic extrusive rocks within the NCOB exhibit boninitic signatures,and may represent the products of subduction initiation magmatism,whereas other extrusive rock occurrences display N-MORB to E-MORB geochemical fingerprints,slightly modified by subduction derived fluids.Using these geochemical data and constraints,we present a tectonomagmatic model for the evolution of the NCOB within the framework of the Caribbean geology.

Studies on the regional feature of organic carbon in sediments off the Huanghe River Estuary waters

Organic carbon (OC) in definitely small area sediments(according to marine dimension)off the Huanghe River Estuary is investigated in order to evaluate the feature of regional difference of physical and chemical properties in marginal sea sediments.The distributions of OC in sediments with natural grain size and the relationship with the pH,Eh ,Es and Fe3+/Fe2+ are discussed.In addition,OC decomposition rates in surfacial/subsurfacial sediments are estimated.OC concentrations range from 0.26% to 1.8%(wt)in the study area. Significant differences in OC content and in horizontal distribution as well as various trends in surfacial /subsurfacial sediments exhibit the feature of regional difference remarkably in marginal sea sediments. The complicated distribution of OC in surface sediments is due to the influence of bacterial activity and abundance, bioturbation of benthos and physical disturbance. The OC decomposition rate constant in surfacial /subsurfacial sediments ranges from 0.009 7 to 0.076 a-1 and the relatively high values may be mainly related to bacteria that are mainly responsible for OC mineralization;meio-and macrofauna affect OC degradation both directly, through feeding on it, and indirectly through bioturbation and at the same time coarse sediments are also disadvantageous to OC preservation.In almost all the middle and bottom sediments the contents of OC decrease with the increase of deposition depth, which indicates that mineralization of OC in the middle and bottom sediments has occurred via processes like SO reduction and Fe-oxide reduction.

On the regional characteristics of cuisine in China

This paper attempts to study regional differentiation of cuisine in China and set up a rudimentary framework for regional characteristic of cuisine This framework is mainly concerned with regional school of cuisine (or cuisine systems) and regional dishes It also gives them some explanations of the process of the formation

A NOVEL REGION FEATURE USED IN MULTISENSOR IMAGE FUSION

A new region feature which emphasized the salience of target region and its neighbors is proposed. In region segmentation-based multisensor image fusion scheme, the presented feature can be extracted from each segmented region to determine the fusion weight. Experimental results demonstrate that the proposed feature has extensive application scope and it provides much more information for each region. It can not only be used in image fusion but also be used in other image processing applications.

Region Pair Grey Difference Classifier for Face Detection

A new kind of region pair grey difference classifier was proposed. The regions in pairs associated to form a feature were not necessarily directly-connected, but were selected dedicatedly to the grey transition between regions coinciding with the face pattern structure. Fifteen brighter and darker region pairs were chosen to form the region pair grey difference features with high discriminant capabilities. Instead of using both false acceptance rate and false rejection rate, the mutual information was used as a unified metric for evaluating the classifying performance. The parameters of specified positions, areas and grey difference bias for each single region pair feature were selected by an optimization processing aiming at maximizing the mutual information between the region pair feature and classifying distribution, respectively. An additional region-based feature depicting the correlation between global region grey intensity patterns was also proposed. Compared with the result of Viola-like approach using over 2 000 features, the proposed approach can achieve similar error rates with only 16 features and 1/6 implementation time on controlled illumination images.

The City Culture and Regional Feature of Osaka

Introduced is the regional feature and development plan of Osaka in Japan with statistic at figures. The paper makes an analysis of the meaning of regional architecture with balanced consideration of modern technology and social development based on its homeland.

Deep learning-based method for detecting anomalies in electromagnetic environment situation

The anomaly detection of electromagnetic environment situation(EMES) has essential reference value for electromagnetic equipment behavior cognition and battlefield threat assessment.In this paper,we proposed a deep learning-based method for detecting anomalies in EMES to address the problem of relatively low efficiency of electromagnetic environment situation anomaly detection(EMES-AD).Firstly,the convolutional kernel extracts the static features of different regions of the EMES.Secondly,the dynamic features of the region are obtained by using a recurrent neural network(LSTM).Thirdly,the Spatio-temporal features of the region are recovered by using a de-convolutional network and then fused to predict the EMES.The structural similarity algorithm(SSIM) is used to determine whether it is anomalous.We developed the detection framework,de-signed the network parameters,simulated the data sets containing different anomalous types of EMES,and carried out the detection experiments.The experimental results show that the proposed method is effective.

MFSR: Maximum Feature Score Region-based Captions Locating in News Video Images

For news video images, caption recognizing is a useful and important step for content understanding. Caption locating is usually the first step of caption recognizing and this paper proposes a simple but effective caption locating algorithm called maximum feature score region （MFSR） based method, which mainly consists of two stages： In the first stage, up/down boundaries are attained by turning to edge map projection. Then, maximum feature score region is defined and left/right boundaries are achieved by utilizing MFSR. Experiments show that the proposed MFSR based method has superior and robust performance on news video images of different types.

A multicenter prospective study of next-generation sequencing-based newborn screening for monogenic genetic diseases in China

Background Newborn screening(NBS)is an important and successful public health program that helps improve the long-term clinical outcomes of newborns by providing early diagnosis and treatment of certain inborn diseases.The develop-ment of next-generation sequencing(NGS)technology provides new opportunities to expand current newborn screening methodologies.Methods We designed a a newborn genetic screening(NBGS)panel targeting 135 genes associated with 75 inborn disorders by multiplex PCR combined with NGS.With this panel,a large-scale,multicenter,prospective multidisease analysis was conducted on dried blood spot(DBS)profiles from 21,442 neonates nationwide.Results We presented the positive detection rate and carrier frequency of diseases and related variants in different regions;and 168(0.78%)positive cases were detected.Glucose-6-Phosphate Dehydrogenase deficiency(G6PDD)and phenylketonuria(PKU)had higher prevalence rates,which were significantly different in different regions.The positive detection of G6PD variants was quite common in south China,whereas PAH variants were most commonly identified in north China.In addi-tion,NBGS identified 3 cases with DUOX2 variants and one with SLC25A13 variants,which were normal in conventional NBS,but were confirmed later as abnormal in repeated biochemical testing after recall.Eighty percent of high-frequency gene carriers and 60%of high-frequency variant carriers had obvious regional differences.On the premise that there was no significant difference in birth weight and gestational age,the biochemical indicators of SLC22A5 c.1400C>G and ACADSB c.1165A>G carriers were significantly different from those of non-carriers.Conclusions We demonstrated that NBGS is an effective strategy to identify neonates affected with treatable diseases as a supplement to current NBS methods.Our data also showed that the prevalence of diseases has significant regional charac-teristics,which provides a theoretical basis for screening diseases in different regions.

A positioning method for maize seed laser-cutting slice using linear discriminant analysis based on isometric distance measurement

It is necessary that vision system should aid laser-cutting manipulator to position the specified part of each maize seed for getting the slice breeding genotype analysis with high throughput.Each of trivial maize seeds should be recognized and positioned in a certain posture.Correlation area ratio(CAR)is defined as the metric of pixel attribute.A large template of round mask is adopted for seed morphological detection to measure the CAR values.We get the feature points extracted from the seed image through the isometric mapping operation.Iterative processes of linear discriminant analysis search the morphological data space to learn non-linear transformations to the space where data are linearly separable.Linear discriminant analysis utilizes the data directional distribution to position the major axis and distinguish different parts of maize seed.The labeling partition operation is applied for picking out the scattered pieces to be finely clustered.Without denoising process,the feature region could be recognized with accuracies by the synthetical methods.Extensive experiments on a large amount of seeds demonstrate the effectiveness of proposed methods.